Explainable Hydrological Monitoring: Large Language Models as Semantic Interpreters of Machine-Learning-Based Water System Intelligence

Machine learning (ML) models have become essential tools for monitoring water system dynamics, enabling accurate prediction of water levels, discharge patterns, and responses to meteorological forcing. However, their operational deployment remains constrained by limited interpretability and the challenge of translating numerical outputs into actionable insight, particularly when assessing system anomalies, regime shifts, and potential impacts on aquatic and riparian habitats.

This study introduces a novel framework that integrates large language models (LLMs) as a semantic interpretation layer within ML-based hydrological monitoring systems. Building on established time-series ML architectures for water level prediction, model outputs are coupled with statistical anomaly detection techniques to identify atypical hydrological behaviour, threshold exceedances, and periods of elevated system stress relevant to near-real-time monitoring. These quantitative signals, together with meteorological drivers and system metadata, are subsequently processed by an LLM to generate structured, contextual natural-language explanations.

The proposed framework is demonstrated using historical water monitoring datasets, with particular emphasis on extreme events and hydrological anomalies. When such events are detected, the LLM synthesizes information across multiple data streams to articulate observed patterns, plausible hydro-meteorological drivers, and potential implications for water system functioning and associated habitats. Rather than replacing process-based understanding or predictive models, the LLM acts as an intelligent synthesis component that contextualizes ML outputs and supports their interpretation.

Results indicate that LLM-enhanced monitoring outputs can substantially improve transparency, interpretability, and communicability compared to conventional numerical monitoring approaches, thereby facilitating improved situational awareness and decision support during critical periods. By embedding natural-language reasoning within data-driven monitoring workflows, this work establishes a pathway toward interpretable, stakeholder-centred hydrological monitoring that aligns advanced artificial intelligence methods with practical environmental observation and management needs.

Keywords

• Hydrological monitoring
• Machine learning interpretability
• Large language models
• Water system intelligence